Temperature control method of large-current connector based on temperature rise management and large-current connector

Abstract

The application discloses a large-current connector temperature control method based on temperature rise management and a large-current connector thereof, relates to the technical field of large-current connector temperature control, and specifically comprises the following steps: in the case that a heat conduction path is limited, collecting thermal state information of the large-current connector in an operation process, and combining an internal structural configuration of the large-current connector, a structural heat distribution model for representing a heat conduction process of a contact area to a metal shell is established; based on the established structural heat distribution model and the collected thermal state information, a thermal information collection deviation degree in the case that the heat conduction path is limited is evaluated; according to the evaluation result, it is judged whether temperature control processing should be performed, and corresponding temperature control processing measures are performed according to the judgment result. The application solves the problem of thermal information collection deviation caused by the limited heat conduction path, and realizes accurate perception of the thermal state of the large-current connector and dynamic temperature control response.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-current connector temperature control, specifically to a high-current connector temperature control method based on temperature rise management and a high-current connector thereof. BACKGROUND

[0002] High-current connector temperature control based on temperature rise management refers to monitoring the temperature change trend of the high-current connector in real time during its operation, combining parameters such as current load, contact resistance, and environmental temperature, dynamically evaluating the temperature rise state of the connector, and implementing active temperature control strategies accordingly to ensure that the connector works within a safe and stable temperature range. Since the contact parts of the high-current connector are prone to significant temperature rise due to resistance heating during continuous high-load operation, if the temperature cannot be controlled in time and effectively, it may lead to a decrease in electrical conductivity, material aging, and even serious faults such as contact ablation and melting, thereby affecting the stability and safety of the entire electrical system. Therefore, temperature control based on temperature rise management not only reflects the thermal risk state of the connector in real time, but also realizes intelligent adjustment and protection of the connector's working state through linkage of cooling equipment, adjustment of current-carrying strategy, or issuance of warning signals, thereby significantly improving the reliability of the connector, prolonging its service life, and ensuring the safe and continuous operation of the power or industrial system.

[0003] The existing high-current connector temperature control technology based on temperature rise management generally involves arranging high-precision temperature sensors at key parts of the connector, collecting temperature data in different operating conditions in real time, combining current information obtained by the current sensor, and using a pre-set temperature rise model or algorithm to dynamically predict and judge the temperature rise trend of the connector. When the temperature rise reaches a set threshold or shows a rapid upward trend, the system will automatically trigger the temperature control mechanism, including starting forced air cooling or liquid cooling devices, reducing system current-carrying, switching backup lines, adjusting the connector contact structure, or sending warning signals, to reduce the local temperature of the connector and prevent problems such as poor contact, material thermal damage, and shortened life caused by overheating. The entire temperature control process generally includes five core links: temperature collection, data processing and analysis, temperature rise prediction and judgment, control decision execution, and feedback optimization, which realize real-time adjustment and safety protection of the working thermal state of the connector through closed-loop control, thereby ensuring the stability of current transmission while improving the intelligent management level and operational reliability of the entire electrical system.

[0004] The existing technology has the following deficiencies:

[0005] In the case of a high-current connector continuously carrying a high current, the internal contact area will first accumulate a large amount of heat. Since the connector is usually covered with a metal shell, the heat conduction path between the contact area and the shell is limited in structure, resulting in heat transfer delay and energy attenuation in the process of heat conduction from the contact area to the outside of the shell. The temperature data collected by the temperature acquisition device arranged outside the shell has a significant deviation. Due to this deviation, even if the contact area is in an overheated state, the external temperature value collected may still be in the normal range, resulting in misreading of the perception of thermal risk in the temperature control process. The existing high-current connector temperature control technology based on temperature rise management cannot accurately determine whether temperature control processing should be performed according to the degree of heat information collection deviation under the condition of limited heat conduction path, and still relies on surface temperature value as the basis for decision-making, resulting in temperature control operation lag or even complete non-triggering. Further, it will cause the contact area to continuously accumulate heat, local ablation, and an increase in contact resistance, eventually leading to serious consequences such as degradation of connector function, breakdown failure, or power interruption.

[0006] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0007] The purpose of the present application is to provide a high-current connector temperature control method based on temperature rise management and a high-current connector thereof to solve the problems in the background.

[0008] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: a high-current connector temperature control method based on temperature rise management, specifically comprising the following steps:

[0009] Detecting the current transmission state of the high-current connector during operation to determine whether there is a situation of continuously carrying a high current;

[0010] In the case of a high-current connector continuously carrying a high current, obtaining the structural parameters of the high-current connector, and determining whether there is a limited heat conduction path between the metal shell and the contact area in structure;

[0011] In the case of a limited heat conduction path, collecting the thermal state information of the high-current connector during operation, and establishing a structural heat distribution model for representing the heat conduction process from the contact area to the metal shell in combination with the internal structural configuration of the high-current connector;

[0012] Based on the established structural heat distribution model and the collected thermal state information, evaluating the degree of heat information collection deviation under the condition of limited heat conduction path;

[0013] According to the evaluation result, it is judged whether the temperature control processing should be performed, and corresponding temperature control processing measures are performed according to the judgment result;

[0014] Based on the adaptation between the evaluation result and the temperature control operation, the generation mechanism of the evaluation result is continuously optimized to enhance the dynamic response capability of the temperature control processing.

[0015] Preferably, in the case that the high-current connector is in a continuous high-current carrying operation, the structural parameters of the high-current connector are obtained, and it is judged whether there is a limited heat conduction path between the metal shell and the contact area of the high-current connector, specifically:

[0016] In the case that the high-current connector is in a continuous high-current carrying operation, the structural parameters of the high-current connector are obtained, specifically including the structural distance between the metal shell and the contact area of the high-current connector, the thermal conductivity of the material contained in the heat conduction path, and the spatial distribution characteristics of the insulation barrier layer.

[0017] When the structural distance exceeds the preset thermal conduction critical length, and the heat conduction path contains a material segment with a thermal conductivity lower than the set thermal conductivity value, and there is a continuously arranged insulation barrier layer segment, it is determined that there is a limited heat conduction path between the metal shell and the contact area of the high-current connector.

[0018] Preferably, in the case that there is a limited heat conduction path, the thermal state information of the high-current connector during operation is collected, and a structural heat distribution model for representing the heat conduction process from the contact area to the metal shell is established based on the internal structure configuration of the high-current connector, specifically:

[0019] In the case that there is a limited heat conduction path, the thermal state information of the high-current connector during operation is collected, specifically including the surface temperature data of the metal shell outside and the load current heat performance parameters generated under the operating current.

[0020] Based on the internal structure configuration of the high-current connector, the contact area is taken as the starting point of the heat source, and the spatial arrangement relationship, material thermal conductivity, structure size information and coverage range of the insulation barrier layer of each structure segment are extracted in turn according to the heat conduction path, and a corresponding structural heat distribution model is established, which is used to represent the heat energy distribution trend, energy attenuation process and spatial position of the blocked part in the heat conduction path from the contact area to the outside of the metal shell.

[0021] Preferably, based on the established structural heat distribution model and the collected thermal state information, the degree of thermal information collection deviation under the limited heat conduction path is evaluated, specifically including the following steps:

[0022] Extracting structural heat conduction configuration information from the established structural heat distribution model, extracting external thermal response dynamic information from the collected thermal state information, and normalizing after extraction;

[0023] Based on the normalized structural heat conduction configuration information and external thermal response dynamic information, respectively generating structural thermal response deviation coefficient and thermal load perception lag index;

[0024] Based on the generated structural thermal response deviation coefficient and thermal load perception lag index, generating the collection deviation index by weighted summation;

[0025] Determine the pre-set collection deviation index threshold interval, and compare it with the generated collection deviation index after determination, and evaluate the degree of thermal information collection deviation under the condition of limited heat conduction path according to the comparison result.

[0026] Preferably, the acquisition logic of the structural thermal response deviation coefficient is as follows:

[0027] Extracting structural heat conduction configuration information from the established structural heat distribution model, specifically including two types of data of theoretical temperature values of each node of the heat conduction path and physical length of the heat conduction path, dividing the two types of data by the respective preset maximum values respectively to obtain the normalized theoretical temperature values of each node of the heat conduction path and the physical length of the heat conduction path, and respectively marking them as and , represents the normalized theoretical temperature value of the th node of the heat conduction path, represents the normalized physical length of the heat conduction path, , is a positive integer;

[0028] Extracting external thermal response dynamic information from the collected thermal state information, specifically including the measured temperature values of the sensors at each node of the heat conduction path, and dividing by the corresponding preset maximum value to obtain the normalized measured temperature values of the sensors at each node of the corresponding heat conduction path, and marking them as , represents the normalized measured temperature value of the sensor at the th node of the corresponding heat conduction path;

[0029] Calculating the structural thermal response deviation coefficient, and the specific calculation formula is as follows:

[0030] In the formula, is the structural thermal response deviation coefficient.

[0031] Preferably, the acquisition logic of the thermal load perception lag index is as follows:

[0032] Extract external thermal response dynamic information from the collected thermal state information, specifically including two types of data: the theoretical heat power generated by the large current carrying in unit time and the temperature rise rate of the metal shell surface in unit time. Divide the two types of data by their respective preset maximum values to obtain the normalized theoretical heat power generated by the large current carrying in unit time and the temperature rise rate of the metal shell surface in unit time, respectively, and label them as and , represents the normalized theoretical heat power generated by the large current carrying in unit time, represents the normalized temperature rise rate of the metal shell surface in unit time.

[0033] Calculate the thermal load perception lag index, and the specific calculation formula is as follows:

[0034] In the formula, is the thermal load perception lag index.

[0035] Preferably, based on the generated structure thermal response deviation coefficient and the thermal load perception lag index , the acquisition deviation index is generated by weighted summation, and the specific calculation formula is as follows:

[0036] In the formula, is the acquisition deviation index, and are non-zero weight coefficients of the structure thermal response deviation coefficient and the thermal load perception lag index , and .

[0037] Preferably, the pre-set acquisition deviation index threshold interval is determined, and after being determined, it is compared with the generated acquisition deviation index , and according to the comparison result, the degree of thermal information acquisition deviation under the condition of limited heat conduction path is evaluated. The specific comparison analysis is as follows:

[0038] If , the degree of thermal information acquisition deviation under the condition of limited heat conduction path is low;

[0039] If , the degree of thermal information acquisition deviation under the condition of limited heat conduction path is moderate;

[0040] If , the degree of thermal information acquisition deviation under the condition of limited heat conduction path is serious.

[0041] Preferably, according to the evaluation result, it is judged whether the temperature control processing should be performed, and corresponding temperature control processing measures are performed according to the judgment result, specifically:

[0042] When the evaluation result is low deviation degree, it is judged that the temperature control processing is not needed to be performed, and the current running state of the large current connector is kept unchanged;

[0043] When the evaluation result is moderate deviation degree, it is judged that the temperature control warning processing needs to be performed, and the temperature control processing measures include limiting the current carrying strength in the set pre-downshift range, and triggering the temperature rise risk reminding instruction;

[0044] When the evaluation result is serious deviation degree, it is judged that the forced temperature control processing must be performed, and the temperature control processing measures include starting the forced cooling device and cutting off the high load channel current supply to reduce the contact area temperature and prevent further thermal runaway.

[0045] Preferably, the large current connector based on temperature rise management includes a high current identification module, a heat conduction constraint judgment module, a thermal characteristic modeling module, a deviation evaluation module, a temperature control decision module and a response optimization module;

[0046] The high current identification module detects the current transmission state of the large current connector in the running process, and judges whether there is a sustained high current carrying operation;

[0047] The heat conduction constraint judgment module, in the case that the large current connector is in a sustained high current carrying operation, acquires the structure parameters of the large current connector, and judges whether there is a heat conduction path limitation between the metal shell and the contact area of the large current connector in structure;

[0048] The thermal characteristic modeling module, in the case that there is a heat conduction path limitation, collects thermal state information of the large current connector in the running process, and establishes a structure heat distribution model for representing the heat conduction process of the contact area to the metal shell in combination with the internal structure configuration of the large current connector;

[0049] The deviation evaluation module, based on the established structure heat distribution model and the collected thermal state information, evaluates the thermal information collection deviation degree in the case of heat conduction path limitation;

[0050] The temperature control decision module, according to the evaluation result, judges whether the temperature control processing should be performed, and corresponding temperature control processing measures are performed according to the judgment result;

[0051] The response optimization module, based on the adaptation between the evaluation result and the temperature control operation, continuously optimizes the generation mechanism of the evaluation result to enhance the dynamic response ability of the temperature control processing.

[0052] In the above technical solution, the technical effects and advantages provided by the present application are:

[0053] 1、The application establishes a structural thermal distribution model, combines the thermal state information in the operation process, evaluates the thermal information collection deviation degree of the high-current connector under the condition of limited heat conduction path, and significantly improves the problem of misjudgment of thermal state caused by relying only on the external temperature collection results of the metal shell. The deviation degree is comprehensively reflected by two quantitative indicators of structural thermal response deviation coefficient and thermal load perception lag index, so that the system can accurately judge whether the contact area is in a thermal risk state, even if the external temperature seems normal, the internal potential overheating can be identified, and the delay or absence of temperature control operation can be effectively avoided.

[0054] 2、The deviation evaluation result is refined into three levels of low deviation, medium deviation and serious deviation, and different temperature control processing strategies are matched for different levels, and a closed-loop control logic of temperature control judgment and decision is constructed. Compared with the traditional temperature control mode triggered by fixed threshold, this scheme has stronger judgment granularity and response flexibility, which can not only give early warning in slight abnormality, but also quickly execute forced cooling and current cut-off in high risk to prevent thermal runaway escalation. At the same time, the evaluation logic can be continuously self-adaptive optimized to realize feedback learning on the actual performance in the operation process, so as to continuously improve the dynamic response capability and adaptation efficiency of the system.

[0055] 3、The application unifies the parameters of the structure (structural distance, material thermal conductivity, insulation layer distribution) and the thermal data (temperature change, current-carrying heat power) into a model-driven evaluation mechanism to form a thermal behavior digital modeling system for high-current connectors. The modeling process, data normalization processing and weighting algorithm are all completed based on objective data that can be collected and calculated, ensuring the high realizability of the system. At the same time, the model has good expansibility and can adapt to different specifications and structural forms of high-current connector application scenarios, has strong universality, strong portability and industrial practical value, and lays a solid foundation for building an intelligent electrical connection management system. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0057] Figure 1 The flowchart of the high-current connector temperature control method based on temperature rise management and the high-current connector of the present application.

[0058] Figure 2 The module schematic diagram of the high-current connector temperature control method based on temperature rise management and the high-current connector of the present application. DETAILED DESCRIPTION

[0059] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the inventive gist to those skilled in the art.

[0060] The present application provides a large current connector temperature control method based on temperature rise management as shown in the following steps: Figure 1 The large current connector temperature control method based on temperature rise management as shown in the following steps:

[0061] Detect the current transmission state of the large current connector during operation, and determine whether there is a sustained high current carrying operation condition;

[0062] The current transmission state of the large current connector can be comprehensively analyzed by collecting the current change data of the large current connector during operation, combining the historical operation conditions, load duration and current carrying trend. In specific implementation, first, the sampling device is used to record the current value in the current carrying channel of the connector in real time, and the continuously collected current data is input into the operation model. Through the indexes such as the average current in the statistical period, the fluctuation amplitude and the high load maintenance time, the characteristic information representing the sustained high current operation state is extracted. Then, a set of combined judgment conditions for representing "sustained high current carrying" is set, including that the average current exceeds the threshold, the time of continuously exceeding the high current threshold meets the set period, and the change rate is stable, and the rule engine or algorithm judgment module is used to determine whether the current operation state meets the characteristic requirements of "sustained high current". All judgment logics can be executed in the form of software algorithm in the controller to automatically identify whether the connector enters the high heat risk operation state that needs temperature control management.

[0063] The reason why it is necessary to accurately judge whether the large current connector is in a sustained high current carrying operation condition is that this state is often a prerequisite for the first occurrence of heat accumulation in the contact area, especially in structures with limited heat conduction paths. The instantaneous current cannot effectively reflect the heat accumulation risk, but by identifying the intensity, stability and sustainability characteristics of the current, an initial evaluation basis highly related to the heat accumulation process can be established. Only when it is judged that the connector is in a high current long-term operation condition, it is necessary to further evaluate the structure heat conduction path and the temperature rise risk degree, so as to ensure that the temperature control process is activated only when there is a real heat hazard. This pre-operation state recognition in the form of software not only improves the accuracy and real-time of temperature control response, but also avoids the false triggering of the temperature control mechanism under low load or instantaneous fluctuation, ensuring that the temperature rise management process is targeted and efficient.

[0064] In the case that the high-current connector is in a continuous high-current carrying operation, the structural parameters of the high-current connector are obtained, and it is judged whether the heat conduction path between the metal shell and the contact area of the high-current connector is limited in structure.

[0065] In the case that the high-current connector is in a continuous high-current carrying operation, the structural parameters of the high-current connector are obtained, and it is judged whether the heat conduction path between the metal shell and the contact area of the high-current connector is limited in structure.

[0066] In the case that the high-current connector is in a continuous high-current carrying operation, the structural parameters of the high-current connector are obtained, and it is judged whether the heat conduction path between the metal shell and the contact area of the high-current connector is limited in structure.

[0067] The structural parameters of the high-current connector can be obtained by associating with the design modeling data or manufacturing configuration information of the high-current connector. Specifically, based on the three-dimensional structure modeling file, structural data list or engineering drawing file provided by the connector when it is shipped, the data such as the position of the contact area, the profile of the metal shell, the arrangement of the insulating layer, the material type and the like are imported into the control software in a standard structured format. In the software environment, by setting the recognition rules, the geometric distance information from the metal shell to the contact area is extracted, the material type and the corresponding thermal conductivity parameters of each component in the heat conduction path are labeled, and the arrangement and coverage length of the insulating material in space are further identified. All structural information is saved in an associated database in electronic form. The control logic reads the structural data of the connector of the corresponding type or number, and completes the automatic acquisition and calling of the structural parameters without relying on manual measurement, so as to ensure that the subsequent temperature rise judgment logic operates on the basis of real and verifiable structure.

[0068] The reason why it is necessary to obtain the structural parameters of the high-current connector under the condition that the high-current connector is continuously carrying high current is that the physical structure of the heat conduction path determines the efficiency and hysteresis of heat conduction from the contact area to the metal shell, and this structure itself is static and cannot be reflected in real time by thermal measurement. Therefore, the geometry and material composition of the connector must be clearly understood, so that when the heat collection data deviates, it can be determined whether the deviation is caused by the heat conduction path itself. Especially in the case where there is a long structural distance between the metal shell and the contact area, the proportion of low thermal conductivity material is large, or the insulating barrier layer is continuously distributed, the heat conduction will be significantly delayed, resulting in that the surface temperature measurement cannot reflect the internal thermal risk. If the structure recognition is not intervened in advance through software, the source of this deviation cannot be effectively modeled and evaluated, and it is also impossible to accurately determine whether there is a risk of "temperature rise misreading", so that the subsequent temperature control decision basis is unreliable. Therefore, this step is an indispensable structure prerequisite in the entire deviation judgment and temperature control management process.

[0069] When the structural distance exceeds the preset heat conduction critical length, and the heat conduction path contains a material segment with a thermal conductivity lower than the set heat conduction value, and there is a continuously arranged insulating barrier layer segment, it is determined that the metal shell of the high-current connector is structurally limited in the heat conduction path between the contact area.

[0070] This judgment logic can be automatically executed in software through a structural parameter analysis model, and the implementation way is: first, extract the geometric path information from the contact area to the metal shell from the structure data, and identify whether the structural distance on the path exceeds the heat conduction critical length through spatial analysis tools; then, the software compares the thermal conductivity from the imported material attribute table segment by segment, identifies whether the structure segment on the path contains a structure segment with a thermal conductivity lower than the specified threshold value; then, call the material layout index to analyze whether there is a continuously arranged insulating barrier layer segment, and verify whether its length meets the set judgment condition. If all three conditions are met, a "heat conduction path limited" marker state is generated in the software. The reason for this judgment is that the longer the structural distance, the stronger the path hysteresis in the heat conduction process; the lower the thermal conductivity of the material, the worse the heat transfer efficiency; and the continuously distributed insulating barrier layer will seriously interfere with the continuity of the heat channel. The three together constitute a typical mode of heat conduction efficiency decline, which is also the physical root cause of the difficulty of heat transfer from the contact area to the shell in time. Therefore, the software takes these three structure dimensions as the trigger point, which can accurately identify whether the current connector has the risk basis of temperature rise response hysteresis.

[0071] The preset heat conduction critical length, the material section with the set heat conduction value, and the continuously arranged insulation barrier section can be uniformly configured and maintained in the software system through engineering experience data combined with experimental statistical models. Specifically, the heat conduction critical length is a structural distance threshold set based on the experimental measurement results of the heat diffusion capacity of different types of large-current connectors under standard load. When the structural path exceeds this length, the time for heat to be transmitted from the source to the shell is significantly delayed. The set heat conduction value is a critical value of a material with high thermal resistance and poor heat transfer selected as an identification threshold by referring to the thermal conductivity coefficients of various commonly used engineering materials (such as nylon, polyimide, engineering plastics, etc.) in thermal working environments. The continuity of the insulation barrier section is based on whether it is arranged in the form of adjacent units in the structural path and whether the cumulative length exceeds a certain proportion of the heat conduction section, for example, more than 1 / 3 of the path length. All these threshold parameters can be preset by professional engineers in the structural analysis platform and integrated in the control software in the form of configuration files, which are automatically called by the algorithm and logically compared with the actual structural parameters, thereby realizing a standardized and automated judgment process.

[0072] In the case where the heat conduction path is limited, the thermal state information of the large-current connector during operation is collected, and a structural heat distribution model for representing the heat conduction process of the contact area to the metal shell is established in combination with the internal structural configuration of the large-current connector.

[0073] In the case where the heat conduction path is limited, the thermal state information of the large-current connector during operation is collected, and a structural heat distribution model for representing the heat conduction process of the contact area to the metal shell is established in combination with the internal structural configuration of the large-current connector.

[0074] In the case where the heat conduction path is limited, the thermal state information of the large-current connector during operation is collected, and a structural heat distribution model for representing the heat conduction process of the contact area to the metal shell is established in combination with the internal structural configuration of the large-current connector.

[0075] The thermal state information of the large-current connector during operation is collected, which can be automatically obtained through the linkage of the thermal monitoring component and the data interface under the software control logic. Specifically, the software first accesses the temperature data stream transmitted by the temperature measuring element arranged outside the metal shell through the standard communication protocol, extracts the surface thermal change value as the first type of data reflecting the external temperature rise trend. At the same time, the software synchronously calls the real-time load current value, voltage value and contact impedance information uploaded by the current monitoring node, and converts them into corresponding current-carrying thermal performance parameters through the thermal power conversion rule as the second type of data. All collected data are stored in time series, sorted by connector number and operating condition, and then called by the software scheduling program for subsequent structure model matching and deviation evaluation processing. The whole collection process is driven by a preset threshold, and the data acquisition process is triggered only when the heat conduction path is limited, which ensures the real-time information acquisition and avoids the waste of redundant monitoring resources.

[0076] The reason why the surface temperature data outside the metal shell and the current-carrying thermal performance parameters must be collected in the presence of a limited heat conduction path is that the limited structure will cause significant attenuation of heat conduction from the contact area to the shell, and only relying on the external temperature value cannot accurately restore the internal heat condition, so it is necessary to introduce the thermal dependent variable and the current-carrying characteristic parameter as the basis for evaluation. The surface temperature, as a direct manifestation of the external thermal response of the connector, can reveal whether there is a heat transfer lag, while the current-carrying thermal parameter is a core index reflecting the internal heat intensity. The combination of the two can establish an effective heat conduction contrast basis. By collecting and storing these two types of data in parallel in the software, it can provide real, continuous and highly correlated data support for subsequent structure thermal distribution modeling and deviation degree evaluation, so as to ensure that the temperature control judgment is based on reliable thermal operation basis, avoid the problem of temperature control decision lag or even failure due to information distortion, and ensure the safety and response accuracy of the large-current connector under complex thermal structure.

[0077] In combination with the internal structure configuration of the large-current connector, the contact area is taken as the starting point of the heat source, and the spatial arrangement relationship, material thermal conductivity, structure size information and coverage range of the insulation barrier layer of each structure section are extracted in turn according to the heat conduction path, and the corresponding structure thermal distribution model is established to represent the heat energy distribution trend, energy attenuation process and spatial position of the blocked part in the heat conduction path from the contact area to the outside of the metal shell.

[0078] The structural thermal distribution model is established in combination with the internal structure configuration of the high-current connector, and can be realized by orderly fusion of the software on the structural parameters and the thermal source positioning data. The specific manner is as follows: the software firstly takes the contact area as the starting point of the heat source, calls the structural parameter file obtained in the early stage, and divides the structural segments according to the physical path of heat spreading from the internal contact area to the metal shell; the spatial arrangement relationship, material thermal conductivity, size characteristics and covering position of the insulation barrier of each structural segment are extracted in turn, and are described by node and linked by heat conduction path in the modeling algorithm in the form of graph structure. Subsequently, the software assigns the node heat capacity and thermal resistance value according to the arrangement order and attributes of the structural segments, completes the structural thermal distribution modeling of the whole heat conduction path in the software environment, and retains the spatial coordinates and attribute labels of each node in the model, which are used to represent the flow trend, strength change and position distribution of the conduction barrier points of the heat energy, so as to build a model path reflecting the heat flow conduction in the real structure.

[0079] The core of the structural thermal distribution model is to map the heat conduction behavior one by one with the internal structure parameters of the high-current connector, and to express it in the form of graph. Among them, the contact area is defined as the heat source point, which corresponds to the source node of the model; each segment of structural material connected to the metal shell and the insulation unit corresponds to the relay node, and each node is attached with specific thermal resistance attribute and spatial coordinate information in the model. The structural segments are linked in physical order to form a link, and if there is a sharp rise in thermal resistance, a low heat capacity or an insulation barrier closed area in the link, a conduction weakening mark is formed in the model. This model no longer only reflects the structure of the connector, but also encodes the actual attenuation process of heat transfer, energy distribution path and limited point position in a logically readable and data-judgable form, so that the subsequent thermal data input can be accurately projected onto the structural nodes to complete the structural restoration and identification of the actual temperature rise deviation path.

[0080] The fundamental reason for establishing the structural thermal distribution model is to solve the problems of path shielding, energy attenuation and delayed feedback in the process of heat conduction from the contact area to the external shell. Due to the complex internal structure of the high-current connector, the thermal conductivity of different materials is greatly different, and the insulation layer may cause thermal isolation, so that even if the shell temperature is normal, the internal heat risk is still difficult to be identified in time. The traditional temperature control strategy is difficult to judge the internal risk degree based on the surface temperature, which leads to delayed or even missed temperature control response. By building the model, the dynamic distribution mapping of the thermal state data in the spatial path can be realized, and a physical basis for subsequent evaluation of the deviation degree of thermal information collection is established. The structural thermal distribution model not only helps to identify the potential heat resistance concentration area, but also serves as a reasoning support chain for temperature rise anomaly judgment, which enhances the accuracy and foresight of temperature control processing. It is a key intermediate link from data-driven to structure-behavior fusion judgment.

[0081] Based on the established structure heat distribution model and the collected heat state information, the degree of heat information collection deviation under the condition of limited heat conduction path is evaluated.

[0082] In this embodiment, based on the established structure heat distribution model and the collected heat state information, the degree of heat information collection deviation under the condition of limited heat conduction path is evaluated, which specifically includes the following steps:

[0083] The structure heat conduction configuration information is extracted from the established structure heat distribution model, and the external thermal response dynamic information is extracted from the collected heat state information, and normalization processing is performed after extraction;

[0084] The extraction of structure heat conduction configuration information and external thermal response dynamic information can be processed by object-oriented data analysis and structure mapping. For the extraction of structure heat conduction configuration information, the topological structure, spatial arrangement, path length, node sequence, material type and corresponding thermal conductivity of each section of the heat conduction path in the established structure heat distribution model can be read to construct a directed heat conduction graph of the structure path section, and each section of the path can be analyzed to encapsulate the physical size, heat conduction parameter, insulation layer coverage and other information of each structure section into a standard structure data unit as configuration information output. For the extraction of external thermal response dynamic information, the temperature time curve of the metal shell surface can be extracted by real-time analysis of the data stream of the temperature sensor collection interface, and the current and voltage sampling results during high current operation can be combined to call the built-in I²R model to calculate the heat input of the current, and the surface temperature rise rate, maximum temperature difference and thermal response delay can be converted into a dynamic thermal response feature vector through an algorithm analysis module. The whole extraction process is automatically completed based on data structure recognition and physical meaning mapping without human intervention, ensuring the uniformity and calculability of the extraction results.

[0085] Based on the normalized structure heat conduction configuration information and the external thermal response dynamic information, the structure heat response deviation coefficient and the thermal load perception lag index are generated respectively;

[0086] Based on the generated structure heat response deviation coefficient and thermal load perception lag index, the collection deviation index is generated by weighted summation;

[0087] The pre-set collection deviation index threshold interval is determined, and after the determination, the generated collection deviation index is compared, and the degree of heat information collection deviation under the condition of limited heat conduction path is evaluated according to the comparison result.

[0088] The determination of the preset acquisition deviation index threshold interval is usually completed by feature clustering and multi-dimensional statistical regression method based on historical sample data. In specific implementation, first, a historical thermal response sample data set containing multiple known running states is constructed, each sample including the combined value of the structure thermal response deviation coefficient and the thermal load perception lag index and its corresponding actual thermal risk label (such as normal, deviation, and serious deviation); then, density clustering (such as DBSCAN) or Gaussian mixture model is used to cluster the sample space, and three natural distribution intervals of the acquisition deviation index are preliminarily divided; then, linear discriminant analysis (LDA) or support vector regression (SVR) method is introduced to construct the fitting boundary in the two-dimensional projection space on the basis of the clustering result, so as to extract the boundary value corresponding to each thermal risk level as the "acquisition deviation index threshold interval"; finally, the threshold interval is embedded in the evaluation module in the form of an interval comparison table to realize automatic identification and comparison. The whole process can be completed by software automatic iterative modeling, and has high precision, adaptability and scalability, and is suitable for dynamic evolving running environment.

[0089] In the embodiment, the acquisition logic of the structure thermal response deviation coefficient is as follows:

[0090] The structure thermal conduction configuration information is extracted from the established structure thermal distribution model, specifically including two types of data of the theoretical temperature values of each node of the heat conduction path and the physical length of the heat conduction path, the two types of data are divided by the respective preset maximum values respectively to obtain the normalized theoretical temperature values of each node of the heat conduction path and the physical length of the heat conduction path, and are respectively marked as and , represents the normalized theoretical temperature value of the th node of the heat conduction path, represents the normalized physical length of the heat conduction path, , is a positive integer;

[0091] Theoretical temperature values of each node of the heat conduction path and the physical length of the heat conduction path can be automatically obtained by a path analysis mechanism in a structural heat distribution model. Specifically, the acquisition of the theoretical temperature values depends on the modeling results of the heat source, which usually takes the contact area as the heat source point, and calculates the theoretical steady-state temperature distribution of each structural node along the heat conduction path by the finite difference or finite element method; the position of each node is determined by the topology of the three-dimensional structure, and the temperature calculation is based on the relationship between the heat flux transmission and the thermal conductivity coefficient in the heat conduction equation, and the output is the theoretical temperature field of the node under the unit current-carrying heat input. The physical length of the heat conduction path is obtained by accumulating the Euclidean distance or path geometric wiring information between the nodes in the structural modeling data, and the system automatically traverses the node sequence and solves the spatial length of each path segment to accumulate the physical total length of the complete heat conduction path. Both types of data can be extracted and stored as vector sequences in real time by the numerical analysis module during the model initialization stage or operation, and have the ability to be fully realized by software.

[0092] The theoretical temperature values of each node of the heat conduction path and the physical length of the heat conduction path are normalized, the main purpose of which is to eliminate the influence of different units and numerical orders of magnitude on the calculation results of the subsequent evaluation formula, so that the output of the structural thermal response deviation coefficient TRD has a unified dimension and comparability. The "preset maximum value" used in the normalization is not a static value set by man, but a boundary parameter automatically generated through historical sample analysis. Specifically, the maximum value of the theoretical temperature value can be extracted from the highest node temperature obtained by simulation under the same type of connector model and similar current-carrying working conditions in the past, and set as the normalization reference; and the maximum value of the physical length comes from the longest effective heat conduction path in the product database of the structural model of the large-current connector, combined with the actual maximum configuration. The preset maximum value is automatically set during the initial modeling or deployment of the system, and can be dynamically updated according to the new data collected, to ensure the stability and generalization ability of the data input after normalization. This processing method not only improves the universality of the model evaluation, but also enhances the adaptability of the software to different product models.

[0093] The external thermal response dynamic information is extracted from the collected thermal state information, specifically including the measured temperature values of the sensors at each node of the corresponding heat conduction path, and divided by the corresponding preset maximum value to obtain the normalized measured temperature values of the sensors at each node of the corresponding heat conduction path, and labeled as , represents the normalized measured temperature value of the sensor at the i-th node of the corresponding heat conduction path;

[0094] ​To acquire the measured temperature values ​​of sensors at each node of the corresponding heat conduction path, the data stream of each temperature measurement point is typically automatically parsed through a thermal state acquisition interface and aligned with the heat conduction path node mapping table. Specifically, the software first establishes a one-to-one mapping relationship between each structural heat conduction path node and the physical sensor location using the structural model and sensor placement configuration table. During operation, the temperature acquisition module periodically acquires the current temperature value of each measurement point from the sensor bus (such as I²C, SPI, or CAN). After data cleaning and anomaly removal, the stable temperature reading of each sensor's corresponding node is extracted. These measured temperature values ​​are automatically stored in the corresponding data vector and spatially matched with the heat conduction path through a path indexing mechanism, thus forming a complete "measured temperature-node location" matrix, providing basic data for subsequent deviation analysis. The entire process requires no manual intervention, relying on the consistency of modeling and structural coordinates to ensure the accuracy and schedulability of the measured data.

[0095] The main purpose of normalizing the measured temperature values ​​is to establish a consistent input space across multiple sensing nodes, preventing distortion of the overall evaluation index due to excessively high or low temperature values ​​at a single sensing point. Normalization ensures that all measured temperature values ​​fall within a unified range of [0,1], which is beneficial for subsequent comparisons with theoretical temperature values ​​and complex function calculations. The "corresponding preset maximum value" used here is a dynamic temperature upper limit set for a specific sensor at each heat conduction path node, not a static fixed value. Its determination typically involves two paths: one is to extract the maximum temperature of the node from historical measured data under typical full-load or overload conditions for this type of connector as the upper limit; the other is to estimate the allowable temperature upper limit based on the material environment (such as shell material and air-cooling path) combined with thermal safety specifications. This preset maximum value can be generated offline by thermal simulation software before software deployment, or it can be dynamically corrected through a monitoring and learning mechanism in the early stages of equipment operation to achieve adaptive matching. Through this normalization step, the measured temperature values ​​are standardized, providing a data foundation for the scientific calculation of the structural thermal response deviation coefficient and significantly enhancing adaptability and generalization under different operating conditions.

[0096] The specific formula for calculating the structural thermal response deviation coefficient is as follows:

[0097] In the formula, This is the structural thermal response deviation coefficient.

[0098] The purpose of this calculation method is to comprehensively evaluate the overall deviation between the theoretical heat distribution and the measured thermal response of high-current connectors under conditions of limited heat conduction paths, in order to reveal the risk of temperature rise misreading caused by structural blockages, thermal attenuation, or sensing failure. In the formula, the first step... The absolute deviation between the theoretical temperature value and the measured temperature value of each heat conduction path node is characterized, which is used to capture the difference in thermal response between the model and the actual. Using the absolute value operation can avoid the offset of positive and negative differences, which may lead to misjudgment. The second step is to take the natural logarithm of the difference value after adding 1, which is to realize the nonlinear buffer mapping of the deviation. When the deviation is small, the function grows slowly, which helps to suppress the interference of small fluctuations on the overall evaluation; when the deviation is large, the function output grows rapidly, which can highlight the influence of significant abnormal nodes. The next step is to multiply the normalized heat conduction path physical length , which physically means that the overall length of the heat conduction path is introduced into the model as an influencing factor to emphasize the thermal resistance amplification brought by long paths and the error amplification effect corresponding to the structural complexity. Finally, the sum of all g nodes is taken and averaged, which is to eliminate the dominant effect of single-point noise on the overall evaluation result, so that TRD becomes a representative average deviation index of the whole path. This formula establishes a good balance between physical meaning, numerical stability and data interpretability, and can provide a stable and engineering meaningful thermal deviation judgment basis for subsequent temperature control strategies.

[0099] The size of the structure thermal response deviation coefficient (TRD) directly reflects the overall difference between the theoretical temperature distribution and the measured temperature response under the condition of heat conduction path limitation, thereby evaluating the actual severity of thermal information acquisition deviation. When the TRD value is small, it means that the theoretical temperature of each node of the heat conduction path and the measured temperature of the corresponding sensor have small deviation, indicating that although the heat conduction is limited by the structure, there is no significant perception deviation, and the temperature rise data can basically accurately reflect the real thermal state of the contact area; on the contrary, when the TRD value is large, it means that most nodes have obvious theoretical-measured temperature difference, especially in structures with long paths or serious insulation barriers, this deviation will be amplified by the path length factor, reflecting that the heat has been significantly attenuated or delayed in the conduction process, and the information obtained by the sensor cannot truly reflect the thermal risk of the contact area. Therefore, the increase of TRD value indicates that the degree of thermal information acquisition deviation is aggravated, which may lead to temperature control lag or misjudgment, and requires dynamic modification and response optimization of the temperature control strategy. TRD value in this model as the core evaluation index, realizes the quantitative mapping between the thermal modeling result and the perception deviation degree, is the key basis for identifying the risk of temperature rise perception failure.

[0100] In this embodiment, the logic of obtaining the thermal load perception lag index is as follows:

[0101] extract external thermal response dynamic information from the collected thermal state information, specifically including two types of data: the theoretical heat power generated by the large current carrying in unit time and the temperature rise rate of the metal shell surface in unit time, divide the two types of data by the respective preset maximum values respectively, and obtain the normalized theoretical heat power generated by the large current carrying in unit time and the temperature rise rate of the metal shell surface in unit time respectively, and mark them as and , represents the normalized theoretical heat power generated by the large current carrying in unit time, represents the normalized temperature rise rate of the metal shell surface in unit time;

[0102] The theoretical heat power generated by the large current carrying in unit time can be calculated by collecting the current data of the connector during the running process through software, and combining the resistance parameters of the large current connector conductor part. This process does not require manual intervention. After the software collects real-time current, it will correspondingly process it with the preset conductor resistance to obtain the current-carrying heat capacity in this time period. The temperature rise rate of the metal shell surface is obtained by collecting multiple consecutive shell outer surface temperature points in a certain interval, and calculating the temperature difference and time difference between adjacent two temperature points, to obtain the temperature rise rate in unit time. These data come from the sensor sampling under the control of the software in the thermal state monitoring process, and can be associated through time stamp to ensure that the heat power and surface temperature rise form a matched input in the same time segment, providing a continuous and reliable dynamic data source for subsequent analysis.

[0103] The purpose of normalization processing is to adjust the data with different numerical ranges and units to the same order of magnitude interval, avoid the imbalance of subsequent calculation weight caused by the difference of original values, and improve the comparability and mathematical stability between parameters. The preset maximum value of the theoretical heat power is usually taken from the maximum power output capacity of this type of large current connector in the experimental test or safety design state. This value can be obtained from prototype test data or product standard data. The maximum value of the temperature rise rate is based on the material thermal inertia and environmental conditions, and the maximum temperature rise slope of the connector under natural or forced cooling conditions is selected as the reference. This value can be extracted through the high quantile point of historical running data. The software will load these maximum values as default reference at the beginning of running, and can be automatically optimized and self-learning corrected through model updating mechanism during running, to ensure that the normalization processing is always consistent with the actual working condition. This processing method not only improves the accuracy of subsequent calculation, but also provides good input guarantee for the perception lag index.

[0104] The calculation formula of the thermal load perception lag index is as follows:

[0105] In the formula, The thermal load perception hysteresis index.

[0106] The calculation formula for the thermal load sensing lag index constructs a dynamic evaluation mechanism for the degree of temperature control response lag by combining normalized theoretical thermal power and temperature rise rate. In the formula, theoretical thermal power reflects the heating trend during high-current operation and is a characterization of the intensity of the internal heat source; temperature rise rate represents the speed at which the metal shell surface responds to heat, and is an external manifestation of whether the system responds to internal thermal loads in a timely manner. When the theoretical thermal power is large and the temperature rise rate is small, it means that the internal heating is rapid but the external sensor response is slow. At this time, the exponential function will produce an amplification effect, causing the overall index to rise rapidly, thereby effectively identifying potential thermal lag risks. Specifically, the exponential calculation part amplifies the weight of "slow response," which can enhance the sensitivity of capturing temperature control judgment lag scenarios; and multiplying it by the theoretical thermal power gives the index an adaptive adjustment capability for different heat source intensities, ensuring that there will be no misjudgment under light load and no missed detection under heavy load. The overall calculation logic not only reflects the dynamic balance between thermal input and thermal sensing, but also constitutes a quantitative evaluation method with high distinguishing ability for thermal lag phenomena in temperature control systems.

[0107] Thermal load perception hysteresis index The magnitude of this index is directly used to assess the degree of lag in thermal information acquisition under conditions of limited heat conduction paths. A larger index indicates a higher theoretical heat power generated by the high-current connector per unit time, while the rate of temperature rise on the metal shell surface is lower. This suggests a significant "internal heating but external response" situation, indicating severe obstruction in the heat conduction path and a lag in the external acquisition device's ability to perceive actual thermal risks, resulting in temperature rise data deviating from the true thermal state. Conversely, a smaller index indicates a relatively good match between heat power and temperature rise changes, better thermal response synchronization, and higher accuracy of the acquired data in reflecting the internal thermal state. Therefore, the magnitude of this index can serve as a core criterion for the degree of lag in thermal information acquisition under conditions of limited heat conduction paths; a higher value indicates a more severe deviation, requiring stronger temperature control intervention.

[0108] In this embodiment, based on the generated structure thermal response deviation coefficient and thermal load perception hysteresis index The acquisition bias index is generated by weighted summation, and the specific calculation formula is as follows:

[0109] In the formula, To collect the deviation index, and These are the structural thermal response deviation coefficients. and thermal load perception hysteresis index The non-zero weight coefficients, and .

[0110] After the calculation of the structural thermal response deviation coefficient (TRD) and the thermal load perception lag index (LPL) is completed, two weight coefficients are introduced to weight and integrate them respectively, thereby generating the acquisition deviation index (ADI). In actual implementation, a set of weight coefficients and corresponding to the evaluation weights of TRD and LPL respectively can be preset to control the proportion of their influence in the final deviation judgment. The TRD mainly measures the importance of the deviation of heat information conduction in the heat conduction path of the structure, and is suitable for connectors with complex structure and significant thermal attenuation; The LPL reflects the mismatch weight between the thermal load and the perceived response, and is more suitable for scenarios with frequent thermal fluctuations and sensitive response lags. Both coefficients are real numbers greater than 0 and less than 1, and always satisfy to ensure the closure and calculation of the evaluation formula. In specific applications, this set of coefficients can be dynamically adjusted through historical thermal working condition data, error feedback curves, or scene-based strategy optimization algorithms to adapt and accurately express the acquisition deviation index to different thermal distortion characteristics. The entire process does not rely on external hardware intervention and can be efficiently executed in embedded algorithms, data processing platforms, or cloud platforms.

[0111] In this embodiment, the pre-set acquisition deviation index threshold interval is determined, and after being determined, it is compared with the generated acquisition deviation index to evaluate the degree of thermal information acquisition deviation under the condition of limited heat conduction path according to the comparison result. The specific comparison and analysis are as follows:

[0112] If , the degree of thermal information acquisition deviation under the condition of limited heat conduction path is low;

[0113] This situation indicates that the error between the theoretical thermal distribution and the measured thermal performance is small, and the heat conduction delay effect under the condition of limited heat conduction path is not significant, and the temperature acquisition point can well reflect the real thermal state of the contact area. In this state, the data relied on by the temperature rise management system has high credibility, indicating that the current temperature control strategy does not need to be adjusted and can continue to operate according to the established parameters, which helps to ensure the long-term stable operation of the connector and avoid excessive intervention.

[0114] If , the degree of thermal information acquisition deviation under the condition of limited heat conduction path is moderate;

[0115] The case shows that there is a certain degree of error in the heat information collection process, but it has not reached the level of serious distortion. In this case, the actual temperature inside the connector may be slightly higher than the temperature feedback by the external sensing point, and the heat conduction lag begins to have an impact, but it has not yet formed a systematic heat hazard. This state prompts the temperature control strategy to remain vigilant and can take appropriate intervention measures, such as reducing the current density, triggering a first-level warning, or adjusting the heat dissipation structure in advance, to suppress the trend of heat accumulation and expansion, thereby controlling the heat risk without affecting the system performance.

[0116] If In the case of limited heat conduction path, the deviation degree of heat information collection is a serious deviation degree.

[0117] This case indicates that there is a significant deviation between the heat distribution model and the measured data, and the lag between the heat power input and the external thermal response is serious, which means that the connector contact area may have experienced a large temperature rise, while the external collection device has not accurately captured it. This state indicates that the temperature rise risk is highly concentrated and the perception has a blind area, which is likely to cause local overheating, increased contact resistance, ablation, material degradation, and other problems. To avoid damage to the connector or interruption of system power supply, forced temperature control measures must be taken immediately, such as cutting off the current, enabling advanced cooling mechanisms, or automatically switching to a backup connection path, to ensure safe operation and prevent fault propagation.

[0118] According to the evaluation result, it is determined whether the temperature control processing should be performed, and the corresponding temperature control processing measures are performed according to the determination result;

[0119] In this embodiment, according to the evaluation result, it is determined whether the temperature control processing should be performed, and the corresponding temperature control processing measures are performed according to the determination result, specifically:

[0120] When the evaluation result is a low deviation degree, it is determined that the temperature control processing does not need to be performed, and the current operating state of the high-current connector remains unchanged;

[0121] When the evaluation result is low deviation degree, the software can realize "judging that no temperature control processing is needed, maintaining the current running state of the high-current connector unchanged". The implementation process includes the following key steps: first, the system compares the deviation index with the preset threshold interval, when the value is lower than the set lower threshold, the software logic will classify the state as low deviation degree, and match to the response strategy of "not triggering temperature control operation" in the internal state mapping table. Then, according to the matching result, the software skips the temperature control control logic in the temperature control decision sub-process, and no longer issues any control instructions to the execution layer, such as not activating the cooling device, not triggering the current load reduction operation, etc., but generates a "maintain status" identifier, and maintains the current running parameters within the set working interval, keeping the device running stably in the current running state. The reason for adopting this judgment logic is that in the low deviation state, the data obtained by the external sensor is highly consistent with the theoretical model calculation result, which means that the heat conduction is not significantly affected, and there is no heat accumulation risk in the contact area, so it is not appropriate to intervene frequently due to small fluctuations, so as to avoid increasing the system energy consumption or unnecessary resource scheduling, so as to realize the balance between the accuracy and economy of the temperature control strategy.

[0122] When the evaluation result is medium deviation degree, it is judged that temperature control warning processing is needed, and the temperature control processing measures include limiting the current-carrying strength within the set pre-reduction gear range, and triggering the temperature rise risk reminding instruction;

[0123] When the evaluation result is medium deviation degree, the software can realize "judging that temperature control warning processing is needed, and the temperature control processing measures include limiting the current-carrying strength within the set pre-reduction gear range, and triggering the temperature rise risk reminding instruction". The specific implementation process is as follows: the software first compares the currently generated collection deviation index ADI with the preset threshold interval, when the value is within the preset threshold interval, it is judged as medium deviation degree, and is mapped to the "warning processing" mode in the built-in deviation level discrimination table of the system. After this mode is triggered, the software will retrieve the corresponding "pre-reduction gear" setting parameters from the set hierarchical current-carrying strategy library, and dynamically limit the amplitude adjustment of the current control command of the high-current connector currently running, to ensure that the actual current-carrying capacity is reduced to an acceptable interval to suppress the possible heat imbalance risk. At the same time, the software will automatically generate a temperature rise risk reminding instruction, and send it to the upper monitoring platform or operation and maintenance terminal through the communication interface, prompting the relevant personnel to pay attention to the connector thermal state change. The design purpose of this strategy is to balance the running continuity and safety, when the deviation does not reach the serious degree, flexible control means and risk prompting mechanism are adopted, which can alleviate the potential heat load and avoid excessive intervention, and improve the intelligent response ability and adaptability of the whole temperature control mechanism.

[0124] When the evaluation result is a serious deviation degree, it is judged that forced temperature control processing must be performed, and the temperature control processing measures performed include starting the forced cooling device and cutting off the high-load channel current supply to reduce the temperature of the contact area and prevent further thermal runaway.

[0125] When the evaluation result is a serious deviation degree, the software can realize the judgment of “forced temperature control processing must be performed” by comparing the collected deviation index ADI with the upper limit in the set threshold interval. When ADI is significantly higher than the upper limit in the set threshold interval, the system determines that the current thermal information collection deviation has been seriously distorted, and there is a high risk of thermal runaway in the contact area. On this basis, the software will automatically call the forced temperature control response chain: on the one hand, the control logic of the cooling execution component associated with the large current connector is activated, for example, by sending an on-off signal to control the start command of the cooling fan, liquid cooling pump or phase change refrigeration unit to quickly improve the local heat dissipation efficiency; on the other hand, the software will simultaneously send a high-load channel flow command to the current control unit, so that the large current path stops power supply before the thermal risk is removed, thereby completely interrupting the heat source input. The core of executing this processing logic is that serious deviation means that the heat conduction information is highly distorted, and the surface temperature value seriously underestimates the real internal temperature rise. If the original running state continues, it is easy to cause catastrophic failures such as carbonization, melting or insulation breakdown in the connector contact area. Therefore, through software to realize fast identification and forced intervention, the local overheating can be prevented from further deterioration to the maximum extent, and the integrity of the electrical connection and the safety of the system operation are protected.

[0126] Based on the adaptation between the evaluation result and the temperature control operation, the generation mechanism of the evaluation result is continuously optimized to enhance the dynamic response capability of the temperature control processing.

[0127] To realize “based on the adaptation between the evaluation result and the temperature control operation, the generation mechanism of the evaluation result is continuously optimized”, a feedback self-adaptive adjustment strategy can be introduced by software. Specifically, after each execution of temperature control processing measures, the software automatically records the matching between the evaluation result (such as the ADI value and the corresponding deviation level) and the temperature control processing measures taken (such as whether to start cooling, load reduction or flow interruption), and continuously monitors the temperature change trend of the large current connector contact area in a short time window after processing. If the temperature rise after processing is successfully suppressed, the system defines this processing as a high adaptability response, otherwise it is defined as a low adaptability. The software will statistically aggregate these response data, dynamically adjust the calculation weight proportion of the structural thermal response deviation coefficient and the thermal load perception lag index through time weighted average or exponential moving average algorithm, so as to gradually converge to generate a more actual thermal risk evolution law. ADI value calculation method.

[0128] The fundamental purpose of this setup is to use the actual temperature control effect to optimize the thermal risk assessment logic, that is, to map historical treatment results back to the parameter modeling mechanism, forming a dynamic adaptive mechanism with a closed loop of "risk-response-feedback". Through this approach, the software system can continuously correct parameter sensitivity and threshold judgment criteria, thereby improving the accuracy of the assessment results in matching the actual risk level, reducing false or delayed triggering, and enhancing the sensitivity and reliability of temperature control. This enables rapid response and intelligent adjustment to thermal risks even when heat conduction paths are limited.

[0129] like Figure 2 The high-current connector based on temperature rise management shown includes a high current identification module, a thermal conductivity constraint determination module, a thermal characteristic modeling module, a deviation evaluation module, a temperature control decision module, and a response optimization module.

[0130] The high current identification module detects the current transmission status of the high current connector during operation and determines whether it is continuously carrying a high current.

[0131] The thermal conductivity constraint determination module acquires the structural parameters of the high-current connector when the high-current connector is operating under continuous high current load, and determines whether there is a thermal conductivity path limitation between the metal shell of the high-current connector and the contact area.

[0132] The thermal characteristic modeling module collects thermal state information of high-current connectors during operation when heat conduction paths are limited, and combines it with the internal structural configuration of high-current connectors to establish a structural thermal distribution model to characterize the heat conduction process from the contact area to the metal shell.

[0133] The deviation assessment module evaluates the degree of deviation in thermal information acquisition under conditions of limited heat conduction paths, based on the established structural thermal distribution model and the collected thermal state information.

[0134] The temperature control decision module determines whether temperature control measures should be implemented based on the assessment results, and then implements the corresponding temperature control measures based on the assessment results.

[0135] The response optimization module continuously optimizes the evaluation result generation mechanism based on the compatibility between the evaluation results and the temperature control operation, so as to enhance the dynamic response capability of temperature control processing.

[0136] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0137] The above-described embodiments can be implemented in part or in whole through software, hardware, firmware or any combination thereof. When implemented in software, the above-described embodiments can be implemented using one or more computer programs written in any suitable programming language. Such programs can be stored in one or more storage media or memory devices (e.g., a hard disk, a floppy disk, a CD, an optical disk, a DVD, a Blu-ray disk, a RAM, a ROM, a flash memory, a cache, etc.) of the computer and loaded into the computer. Such programs can also be loaded into the computer from one or more external sources through a network or a communication channel. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus.

[0138] It should be understood that the sequence of the above processes is not intended to mean the execution order, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0139] Those skilled in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0140] In several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments are merely illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment of the present application according to actual needs.

[0142] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0143] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A temperature control method for a high current connector based on temperature rise management, characterized in that, Specifically comprising the following steps: Detecting the current transmission state of the high-current connector during operation to determine whether there is a sustained high-current carrying operation; In the case of sustained high-current carrying operation of the high-current connector, the structural parameters of the high-current connector are obtained to determine whether there is a situation that the heat conduction path between the metal shell and the contact area of the high-current connector is limited in structure; In the case of limited heat conduction path, the thermal state information of the high-current connector during operation is collected, and combined with the internal structure configuration of the high-current connector, a structural heat distribution model is established to represent the heat conduction process from the contact area to the metal shell; Based on the established structural heat distribution model and the collected thermal state information, the degree of thermal information collection deviation under the condition of limited heat conduction path is evaluated; Specifically comprising the following steps: Extract structural heat conduction configuration information from the established structural heat distribution model, and extract external thermal response dynamic information from the collected thermal state information, and then normalize the extracted information; Based on the normalized structural heat conduction configuration information and external thermal response dynamic information, a structural thermal response deviation coefficient and a thermal load perception lag index are generated respectively; The acquisition logic of the structural thermal response deviation coefficient is as follows: extracting structural heat conduction configuration information from the established structural heat distribution model, specifically including two types of data of theoretical temperature values of each node of the heat conduction path and physical length of the heat conduction path, dividing the two types of data by respective preset maximum values to respectively obtain normalized theoretical temperature values of each node of the heat conduction path and physical length of the heat conduction path, and respectively marking as and , representing the normalized theoretical temperature value of the first node of the heat conduction path, representing the normalized physical length of the heat conduction path, , is a positive integer; The external thermal response dynamic information is extracted from the collected thermal state information, specifically including the measured temperature values of the sensors at each node of the corresponding heat conduction path, and dividing by the preset maximum value corresponding thereto to obtain the normalized measured temperature values of the sensors at each node of the corresponding heat conduction path, and labeling as , represents the normalized measured temperature value of the sensor at the first node of the corresponding heat conduction path; and ​ The structural thermal response deviation coefficient is calculated, and the specific calculation formula is as follows: In the formula, is the structural thermal response deviation coefficient; The acquisition logic of the thermal load perception lag index is as follows: Extract external thermal response dynamic information from the collected thermal state information, specifically including two types of data: the theoretical heat power generated by the large current carrying in unit time and the temperature rise rate of the metal shell surface in unit time. Divide the two types of data by their respective preset maximum values to obtain the normalized theoretical heat power generated by the large current carrying in unit time and the temperature rise rate of the metal shell surface in unit time, respectively, and label them as and , represents the normalized theoretical heat power generated by the large current carrying in unit time, represents the normalized temperature rise rate of the metal shell surface in unit time. The thermal load perception hysteresis index is calculated according to the following formula: In the formula, is the thermal load perception hysteresis index; Based on the generated structural thermal response deviation coefficient and thermal load perception lag index, a collection deviation index is generated by weighted summation; Determine the pre-set collection deviation index threshold interval, and compare it with the generated collection deviation index after determination, and evaluate the degree of thermal information collection deviation under the condition of limited heat conduction path according to the comparison result; According to the evaluation result, it is judged whether the temperature control processing should be performed, and the corresponding temperature control processing measures are executed according to the judgment result; Based on the adaptation between the evaluation result and the temperature control operation, the generation mechanism of the evaluation result is continuously optimized to enhance the dynamic response ability of the temperature control processing.

2. The temperature rise management based large current connector temperature control method of claim 1, wherein, In the case of sustained high-current carrying operation of the high-current connector, the structural parameters of the high-current connector are obtained to determine whether there is a situation that the heat conduction path between the metal shell and the contact area of the high-current connector is limited in structure, specifically: In the case of sustained high-current carrying operation of the high-current connector, the structural parameters of the high-current connector are obtained, including the structural distance between the metal shell and the contact area of the high-current connector, the thermal conductivity of the materials contained in the heat conduction path, and the spatial distribution characteristics of the insulation barrier layer; When the structural distance exceeds the pre-set thermal conduction critical length, and the heat conduction path contains a material segment with thermal conductivity lower than the set thermal conductivity value, and there is a continuously arranged insulation barrier layer segment, it is determined that there is a situation that the heat conduction path between the metal shell and the contact area of the high-current connector is limited in structure.

3. The temperature rise management-based large current connector temperature control method of claim 2, wherein, In the case of limited heat conduction path, the thermal state information of the high-current connector during operation is collected, and combined with the internal structure configuration of the high-current connector, a structural heat distribution model is established to represent the heat conduction process from the contact area to the metal shell, specifically: In the presence of a limited heat conduction path, the thermal state information of the high-current connector during operation is collected, including surface temperature data outside the metal shell and load current generated under the operating current; Combined with the internal structure configuration of the high-current connector, the contact area is taken as the starting point of the heat source, and the spatial arrangement relationship, material thermal conductivity, structure size information and coverage range of the insulation barrier layer of each structure section are extracted in turn according to the heat conduction path, and the corresponding structure heat distribution model is established to represent the heat energy distribution trend, energy attenuation process and spatial position of the blocked part in the heat conduction path from the contact area to the outside of the metal shell.

4. The temperature rise management-based large current connector temperature control method of claim 3, wherein, Based on generating structural thermal response bias coefficient and thermal load perception lag index , the acquisition bias index is generated by weighted summation, and the specific calculation formula is as follows: In the formula, is the acquisition bias index, and are non-zero weight coefficients of structural thermal response bias coefficient and thermal load perception lag index respectively, and .

5. The temperature rise management-based large current connector temperature control method of claim 4, wherein, Determine the preset acquisition deviation index threshold interval And after determining, compare with the generated acquisition deviation index According to the comparison result, evaluate the heat information acquisition deviation degree under the condition of limited heat conduction path. The specific comparison analysis is as follows: If In the case of limited heat conduction path, the deviation degree of heat information collection is low. If The deviation degree of the heat information collection under the condition of limited heat conduction path is moderate. If In the case of limited heat conduction path, the deviation degree of heat information collection is serious deviation degree.

6. The temperature rise management-based large current connector temperature control method of claim 5, wherein, According to the evaluation result, it is judged whether the temperature control processing should be performed, and the corresponding temperature control processing measures are executed according to the judgment result, specifically: When the evaluation result is low deviation degree, it is judged that the temperature control processing is not needed, and the current operating state of the high-current connector is kept unchanged; When the evaluation result is moderate deviation degree, it is judged that the temperature control warning processing needs to be performed, and the temperature control processing measures include limiting the load current intensity in the set pre-downshift range, and triggering the temperature rise risk reminding instruction; When the evaluation result is serious deviation degree, it is judged that the forced temperature control processing must be performed, and the temperature control processing measures include starting the forced cooling device and cutting off the high-load channel current supply to reduce the temperature of the contact area and prevent further thermal runaway.

7. A temperature rise management based high current connector for implementing the temperature rise management based high current connector temperature control method of any of claims 1-6, wherein, It includes a high-current identification module, a heat conduction constraint judgment module, a thermal characteristic modeling module, a deviation evaluation module, a temperature control decision module and a response optimization module. The high-current identification module detects the current transmission state of the high-current connector during operation, and judges whether there is a sustained high-current carrying operation; The heat conduction constraint judgment module, in the case of sustained high-current carrying operation of the high-current connector, acquires the structure parameters of the high-current connector, and judges whether there is a limited heat conduction path between the metal shell and the contact area of the high-current connector in structure; The thermal characteristic modeling module, in the presence of a limited heat conduction path, collects the thermal state information of the high-current connector during operation, and combines the internal structure configuration of the high-current connector to establish a structure heat distribution model for representing the heat conduction process of the contact area to the metal shell; The deviation evaluation module evaluates the thermal information collection deviation degree under the limited heat conduction path based on the established structure heat distribution model and the collected thermal state information; The temperature control decision module, according to the evaluation result, judges whether the temperature control processing should be performed, and executes the corresponding temperature control processing measures according to the judgment result; The response optimization module continuously optimizes the generation mechanism of the evaluation result based on the adaptation between the evaluation result and the temperature control operation, so as to enhance the dynamic response ability of the temperature control processing.

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